Natural Language Processing Basics

Duration: 5 min

This module introduces the fundamentals of Natural Language Processing (NLP), a subfield of AI that focuses on the interaction between computers and humans through natural language. Understanding NLP is crucial for developing applications that can understand, interpret, and generate human language, which is essential for tasks like sentiment analysis, machine translation, and chatbots.

Tokenization

Tokenization is the process of breaking down text into smaller units called tokens, which can be words, phrases, or symbols. This is a fundamental step in NLP as it prepares text data for further processing. Tokenization helps in normalizing text, removing punctuation, and preparing data for algorithms that require structured input.

import nltk
nltk.download('punkt')
from nltk.tokenize import word_tokenize

# Sample text
text = "Natural Language Processing is fascinating."

# Tokenizing the text
tokens = word_tokenize(text)

print(tokens)

Try it in Google Colab:

['Natural', 'Language', 'Processing', 'is', 'fascinating', '.']

Stop Words Removal

Stop words are common words like 'the', 'is', 'and' that do not carry much meaning and are often removed from text data to reduce noise and improve the efficiency of algorithms. Removing stop words helps in focusing on the more meaningful words in the text.

from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

nltk.download('stopwords')

# Sample text
text = "Natural Language Processing is fascinating."

# Tokenizing the text
tokens = word_tokenize(text)

# Loading stop words
stop_words = set(stopwords.words('english'))

# Removing stop words
filtered_tokens = [word for word in tokens if word.lower() not in stop_words]

print(filtered_tokens)

💡 Tip: Always ensure that the text is tokenized before removing stop words to avoid errors.